{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5008b803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import copy\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "# 防止内核挂掉\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d266a80",
   "metadata": {},
   "source": [
    "# Angular Spectrum Propagation (phase & amplitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "351f8955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('Using Device: ',device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3966ea17",
   "metadata": {},
   "source": [
    "### ***Loading and Viewing Dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "414a3ef3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [100/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [200/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [300/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCH_SIZE = 200\n",
    "IMG_SIZE = 32\n",
    "N_pixels = 64\n",
    "PADDING = (N_pixels - IMG_SIZE) // 2  # 避免边缘信息丢失\n",
    "\n",
    "# 数据预处理并加载\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "                                transforms.Pad([PADDING, PADDING], fill=(0), padding_mode='constant'),\n",
    "#                                 transforms.Normalize((0.1307,), (0.3081,))\n",
    "                               ]\n",
    "                               )\n",
    "train_dataset = torchvision.datasets.MNIST(\"./data\", train=True, transform=transform, download=True)\n",
    "val_dataset = torchvision.datasets.MNIST(\"./data\", train=False, transform=transform, download=True)\n",
    "train_dataloader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_dataloader = DataLoader(dataset=val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# 定义一个绘图函数\n",
    "def image_plot(image, label):\n",
    "#     cmap='RdBu'\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.imshow(np.round(image.cpu().numpy(), 5)) # 显示图片每个像素点的振幅\n",
    "    ax.axis('off')\n",
    "    ax.set_title(label.cpu().numpy())\n",
    "#     fig.colorbar(plt.cm.ScalarMappable(cmap=cmap))\n",
    "    plt.show()\n",
    "\n",
    "for i, (images, labels) in enumerate(train_dataloader):\n",
    "    images = images.to(device)\n",
    "#     images_E = torch.sqrt((torch.squeeze(images)+1)/2)\n",
    "    images_E = torch.sqrt(torch.squeeze(images))\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    '''\n",
    "        每一个周期，共300个批次（i=0~299），一共60000个数据；\n",
    "        train_dataloader包含300个批次，包括整个训练集；\n",
    "        每一批次一共200张图片，对应200个标签, len(images[0])=1；\n",
    "        images包含一个批次的200张图片（image[0].shape=torch.Size([1,28,28])），labels包含一个批次的200个标签，标签范围为0~9\n",
    "    '''\n",
    "\n",
    "    #每100个批次绘制第一张图片（含shuffle导致每次运行结果不一样）\n",
    "    if (i + 1) % 100 == 0:\n",
    "        classes = torch.unique(labels).cpu().numpy()\n",
    "        classes_num = len(classes)\n",
    "        print('batch_number [{}/{}]'.format(i + 1, len(train_dataloader)))\n",
    "        print('classes of the first batch: {}, number of classes: {}'.format(classes, classes_num))#  第一个batch的总类\n",
    "#         image_plot(images_phase[0], labels[0])\n",
    "        image_plot(images_E[0], labels[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "563cd417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f9b3c7",
   "metadata": {},
   "source": [
    "### ***Diffractive Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0bcfe12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Diffractive_Layer(torch.nn.Module):\n",
    "    # 模型初始化（构造实例），默认实参波长为532e-9，网格总数50，网格大小20e-6，z方向传播0.002。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.02])):\n",
    "        super(Diffractive_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dd1e4ca2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WA4BKFuknA845ff7zn9cjjzyi7du3q6OjY8L9HR0damtr07Zt23TJJZdIksbGxtTb26u7777bPGc6nVY67YeMAAAAYOqUY50nxWytFyVB3Ty++O/qgtDUeytUMOS8VoBgyvh4EPJYQUOD15Zvqjf75o3k/bFT7PfFWIs/hrEZ/hjGmu1xJcaLfx0CM4DQ71czbD+HqQa/PdlkhyVaAYSJjBE0KClpvGbBkD9Wl7WPN1nhf5Jk7PYQ5O1QQGe9vxJ+X3PngWN3hI/PO/Ek/54IJSxKpILArbfeqocfflg//OEP1dTUVKgQt7S0qL6+XkEQaM2aNeru7lZnZ6c6OzvV3d2thoYGXX/99WWZAAAAACaPdR4AxE+kgsCmTZskSStWrJjQ/sADD+iP//iPJUl33HGHRkZGdMstt+jw4cNasmSJtm7dyt60AAAAFYx1HgDET+BCN5k8OdibFgDKL8r+tABQStN6rcdPBpQ/ZYbZd2p/MmA2m4r/yYD9kSmV8S+BT46G9I3yk4GhUa8tGBr22tzIiHl8/shRo9EeV1BjvOZhPxnIGe3GzwBc2M8TovxkYLIq62PulIqyzjvhXQYAAAAAAED1ivSTAaBckjNbvLbxhR1GT+nQhX41emym3y8f4UuHxJjdXveWX1msOeK3zXx+yD7vwT6vLXfozeIHBgAAYs+8GiDkagLzaoBESF/rigbjCoGgoc48Pt/S6LXlmuy+2Ub/vJlT7I8i1tUAmZl+W9ZfEkqSgpzVaPdNZP07rOPHM/YJUsN+e2rE/ma6ts7vWzMU8tqMW1ceGAvWcfs5DJJ+UKCT9cTIvrLFOF6yn0ZnnNYKGpRCwgan8qoBeLhCAAAAAACAGKIgAAAAAABADFEQAAAAAAAghigIAAAAAAAQQxQEAAAAAACIIXYZQGVoPd1revkqOzr2T/7gca/tM83/6rXNToZEzxp+nfX3dZWkb7+1zGv7lzc/4LW9/JO55vGzn/GTdutfsMeVf/0Nv23U34MWAABUACuZPdLx/vdy5m4CIX0V1tc8PqSvsf98UJf22lxjvXl4vqHWa8ul7XT68Ua/PdNsjyvb6LePzfST+/Mhn2QCI7Te2g1AkpIjRqPxdGftp0DO3AEiZFzOP7F5vKRE1n9uExn/tQnyIQn9mYz/WCMhuwyYAyj+e+Nidx6Q7N0HzJ0HwkTZkcB63zt7B4g44woBAAAAAABiiIIAAAAAAAAxREEAAAAAAIAYoiAAAAAAAEAMESqIipCdNcNra7/iZbPvHzT5AYJ7xk7x2g6On2Ye35nu89rOrbEDXb50+r94bcnTn/ba9p9lHq4/fu5Gr+3tH9kBhHO2+mE7+f2/tk8MAABgMcLgAiM8UJKCOj/82NX7wXW5GX6bJI3PqPHaskZ4oCSNzvTHZYUHSlLWH5Zy9UaoYI0dEOeM9iBnjys5agQYtvjHj82yU/JSA/5502/b87JCEPOpkLDDUb9z6m3/sYKw8L+0EUA4Nm73NQQhQZTOejwrqC8svM8IELSCBiXJ5csQABgWsBnjsEGuEAAAAAAAIIYoCAAAAAAAEEMUBAAAAAAAiCEKAgAAAAAAxBAFAQAAAAAAYohdBlARnBH4mU5mzb6v5+q9ttue+bTX1vA//Z0LJClnBOVmTrOTRbO/k/Ha2me/7T9+xxPm8f/3BQ95bf9nyzVm3wNNZ/qP9Z9/bfYFAADTUGB/VxckjfaknZof1Nb6bWm/TZJktOeb/Ij/8Wb7eGtHgVxtSMK+dQo7XF7ZGcaOAqePeW3pejs1P5XydwQYGW62xzVs7DJg7Chw6ty3zePfqmsyWu3nyxkJ99bOA5K9K0LNkP/a1GTtJzHI+nNwYe8Z6/2VCtmZwkjjd/5LoyAfsnOATnKaf4x3EwjDFQIAAAAAAMQQBQEAAAAAAGKIggAAAAAAADFEQQAAAAAAgBgiVBBV55+OLPAbDzR6Ta3/146iz5k8/xyz/chZLX5be6vXdu+1K8zjHzj3//Ha7pz/A7PvfX+43Gv7cdtlZt8P/vlTZjsAACiSEfA2+XOGhAImjMcy+prhbpIZIBiEBMTJeqyQvi7lt7sao691TkmBkRuXT9l9rbDBbIPZVWOn+YF46To/QLClccQ+gWF03B5XywF/Erl6/zmo77DDrpO1/vH5upCwauMULuS5dcZ7ofZojdeWGDfSsiUlx4zAxdCgP1+QDnl/WX2LPOexvv64nP9yH+ub8Mfr8iF/Iy4kodI7aYS/+5gEEHKFAAAAAAAAMURBAAAAAACAGKIgAAAAAABADFEQAAAAAAAghggVREXI1fnBJR897Vdm3yM5PzwlOTq5YKDc3n1me/1ev63pzA94bS/NaDeP393ht69uOGT2/V9nPem17ej0HwsAAERQjvBAKTRAsCyMcDMXEngW5IyENqtNMkPmgpxx3nzx4Wr5mrCQPL+tdtA+x/hbfuexnJ9AeCRlB8nNmnHUa7vo8hfMvrtmnum1zX/Un++Lv3OqebwSfl8jC0+SlDc+eYXlSObq/LZMs/+8JEdqzePT4/V+36EZZl83XHw4Y8Wy/h6LDRoMPWfIvx3TLGyQKwQAAAAAAIghCgIAAAAAAMQQBQEAAAAAAGKIggAAAAAAADFEQQAAAAAAgBhilwFUnQ/P2O+1/bfmZVP2+Nn9v/ba2v+z3yZJvZ88x2tbVveq2XeWkb7bUj8aZWgAAODdJpsIHpo0XnyCucv738EFRhS9C9kMwJpDYOwQcOyxjAVFctzsG1i7BxiPlcjaz6Eb99uth5ekxJjfNuM1e8L5Wv8kuQG/LTPUbB6fvdhPzf8fH/yJ2ffQB/wdCS7N/5nXlnzL/tiUa8l6bdaOCpKUazB2dQj5fjY54r/vxv2NFpSrC/l+N2G019TYfeuN91cq5GOi8b5zCX/CQdJ+ba33Z9g+INbfg/V3c+y8ZUj+n2a7CYThCgEAAAAAAGKIggAAAAAAADFEQQAAAAAAgBiiIAAAAAAAQAwRKoiKkBz1U0OePHSW2fePztrltTV3Hvbajv7BEvP4xv/xLxFHd+J+fmie1/aWlR4o6fSkH5JSn7JDgPKLzvfa3K69EUcHAADeVymCxYx0NOeMKLXAfqxA/trBhXytF+SMJDarTTID4sxQwbGQgLikP4ewQL28kWeXq7Xj5Ky5BcYQ2p62A+YG+9q8tptOXWr2/U+zfua1nb/goNf24uFTzOMD4zU7Mlhv9nVZf2JG07Hzjvt35I3nK5e2T2AFMyZr7I9+ZkBlKuSFNN6LgRFg6PIh77mcEUqY9YMZf3MSoynk79EK+YxJKOBkcYUAAAAAAAAxREEAAAAAAIAYoiAAAAAAAEAMRSoIbNq0SRdeeKGam5vV3NyspUuX6rHHHivc75xTV1eX2tvbVV9frxUrVmjvXn7XDAAAUOlY5wFA/EQqCMydO1d33XWXnnnmGT3zzDP62Mc+pmuuuabwfwbr16/Xhg0btHHjRu3cuVNtbW1auXKlhoaGyjJ4AAAAlAbrPACIn0i7DFx99dUT/vsb3/iGNm3apKeeekoLFizQPffco3Xr1um6666TJG3evFmtra16+OGHddNNN5Vu1Jh2Elk/GfTVwWaz77CRyvufPviU1/Zfl/8H8/hz/u1cry3///3b+w3xhBwabPTahvK1Zt/5xo4C8xr93RMkafeC+V7bTH/zBQAAisY6rwQCOzXf7ut/Lxckij8+iPBYbtxOcQ+M9sSo0Tfksawk+zC5Or/taLv93eRwm58O79pGvbaRVuOkkpr3+8c/vfkSs+/WhRd4bTPbB722ztPeMI+vTfpp+q/Wt5h93zzS4LWN1tjrwlwm7bWNN/qvw3iD/drk0v5rY2z0cIy1y4Cxm8CxvsZrY+0SELazxbixg1ZIX2e1s3NAyZ1whkAul9OWLVt09OhRLV26VAcOHFBfX59WrVpV6JNOp7V8+XLt2LGjJIMFAABA+bHOA4B4iHSFgCTt2bNHS5cu1ejoqGbMmKFHHnlECxYsKPyfQWtr64T+ra2tevHFF0PPl8lklMlkCv89OOhX5AAAAFB+pV7nSaz1AKCSRb5C4JxzztHu3bv11FNP6XOf+5xuvPFGPf/884X73335knPuPS9p6unpUUtLS+E2b968qEMCAABACZR6nSex1gOASha5IFBbW6uzzjpLixcvVk9Pjy666CJ961vfUltbmySpr69vQv/+/n6vmvxOa9eu1cDAQOF28ODBqEMCAABACZR6nSex1gOAShb5JwPv5pxTJpNRR0eH2tratG3bNl1yybHQjrGxMfX29uruu+8OPT6dTiud9kMzEC+pl9/02up+cIbZd+d5fvvyxn1e2/+7wA+JkaRXr/SPnz3z4vcZ4fsI+XakttYPwEkEVnCLNCPhB+OcWX/I7Puzuf7jzXyP4QEAcCImu86TKnitFyUAcNKPVWSAoNFPkoKk0Z6I8L1eSBCbe8dPOQqMMLsgaT9XiXH/o0RqxH6s5Jh/DhcyhaQxrHzSP29+jr/OkqTDM/1xJY7aIXkzDvh9B8Zmem2/MkKtJWnezLe9toaaMbPvSNqP9cuM2qGCxQpZVioxbtxhhgdKLuuH9wVh75mcfw436r8ObtR4ESW5ceO5CftbtP4eovzZupAnpxyqOOwwUkHgK1/5ilavXq158+ZpaGhIW7Zs0fbt2/XjH/9YQRBozZo16u7uVmdnpzo7O9Xd3a2GhgZdf/315Ro/AAAASoB1HgDET6SCwOuvv67Pfvazeu2119TS0qILL7xQP/7xj7Vy5UpJ0h133KGRkRHdcsstOnz4sJYsWaKtW7eqqampLIMHAABAabDOA4D4iVQQ+Pa3v/2e9wdBoK6uLnV1dU1mTAAAAJhirPMAIH4ihwoCAAAAAIDqN+lQQaAUcm/44Xmn/8Tu+3985H/x2v7LFf/Na7u/c4t5/I9vPttr+0Hfxe89wPeRCOwgkf86b6vXtqDGD245xg+amV9rhwoOz88WPTYAACpSEExtsN9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\n",
      "text/plain": [
       "<Figure size 1500x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看看一个样本经过衍射层后变成啥样\n",
    "model = Diffractive_Layer().to(device)\n",
    "out = model(images_E)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.imshow(images_E[0].squeeze().cpu())\n",
    "ax2.imshow(torch.abs(out[0]).cpu())\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "de95f338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(96.9473, device='cuda:0')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原图像总光强\n",
    "torch.pow(abs(images_E[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2c8b23bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(96.9473, device='cuda:0', dtype=torch.float64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出图像总光强\n",
    "torch.pow(abs(out[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971a347f",
   "metadata": {},
   "source": [
    "### ***Propagation Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f9cdc0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Propagation_Layer(torch.nn.Module):\n",
    "    # 与上面衍射层大致相同，区别在于传输层是最后一个衍射层到探测器层间的部分，中间可以自定义加额外的器件。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.001])):\n",
    "        super(Propagation_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e430c8",
   "metadata": {},
   "source": [
    "### ***Detectors Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "606fd717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22e552a66d0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成一行探测器。指定探测器个数N_det，在x方向上生成齐高等间距det_step的一组探测器\n",
    "# left，right，up和down分别是该行矩形探测器的四个顶点坐标。\n",
    "def generate_det_row(det_size, start_pos_x, start_pos_y, det_step, N_det):\n",
    "    p = []\n",
    "    for i in range(N_det):\n",
    "        left = start_pos_x+i*(int(det_step)+det_size)\n",
    "        right = left + det_size\n",
    "        up = start_pos_y\n",
    "        down = start_pos_y + det_size\n",
    "        p.append((up, down, left, right))\n",
    "    return p\n",
    "\n",
    "# 生成三行探测器。利用generate_det_row函数生成三行等间距矩形探测器。\n",
    "def set_det_pos(det_size = 20, start_pos_x = 46, start_pos_y = 46, \n",
    "                N_det_sets = [3, 4, 3], det_steps_x = [5, 3, 5], det_steps_y = 5):\n",
    "    p = []\n",
    "    for i in range(len(N_det_sets)):\n",
    "        p.append(generate_det_row(det_size, start_pos_x, start_pos_y+i*(det_steps_y+1)\n",
    "                                  *det_size, det_steps_x[i]*det_size, N_det_sets[i]))\n",
    "    return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# def set_det_pos(det_size=20, start_pos_x = 46, start_pos_y = 46):\n",
    "#     p = []\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y, 2*det_size, 3))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+3*det_size, 1*det_size, 4))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+6*det_size, 2*det_size, 3))\n",
    "#     return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# 获取最终衍射光强在各个探测器上的分布情况\n",
    "def detector_region(Int):\n",
    "    detectors_list = []\n",
    "    total = 0\n",
    "    full_Int = Int.sum(dim=(1,2)) # 统计总光强\n",
    "    for det_x0, det_x1, det_y0, det_y1 in detector_pos: # 计算各个探测器区间内的光强占比\n",
    "        detectors_list.append((Int[:, det_x0 : det_x1, det_y0 : det_y1].sum(dim=(1, 2))/full_Int).unsqueeze(-1))\n",
    "    for ele in range(0, len(detectors_list)):\n",
    "        total = total + detectors_list[ele]\n",
    "    return torch.cat(detectors_list, dim = 1)/total\n",
    "\n",
    "# 指定生成的十个探测器的位置。\n",
    "# detector_pos = [\n",
    "#     (46, 66, 46, 66),\n",
    "#     (46, 66, 106, 126),\n",
    "#     (46, 66, 166, 186),\n",
    "#     (106, 126, 46, 66),\n",
    "#     (106, 126, 86, 106),\n",
    "#     (106, 126, 126, 146),\n",
    "#     (106, 126, 166, 186),\n",
    "#     (166, 186, 46, 66),\n",
    "#     (166, 186, 106, 126),\n",
    "#     (166, 186, 166, 186)\n",
    "\n",
    "# 定义探测器模型基本参数\n",
    "det_size = 4\n",
    "det_pad = (N_pixels - 13*det_size)//2\n",
    "detector_pos = set_det_pos(det_size, det_pad, det_pad)\n",
    "\n",
    "# 定义探测器层的图片张量\n",
    "labels_image_tensors=torch.zeros((10,N_pixels,N_pixels), device = device, dtype = torch.double)\n",
    "for ind, pos in enumerate(detector_pos):\n",
    "    pos_l, pos_r, pos_u, pos_d = pos\n",
    "    labels_image_tensors[ind, pos_l:pos_r, pos_u:pos_d] = 1 # 设置探测器区域\n",
    "    labels_image_tensors[ind] = labels_image_tensors[ind]/labels_image_tensors[ind].sum() # 归一化探测器层\n",
    "det_ideal = labels_image_tensors.cpu().numpy().sum(axis = 0)\n",
    "    \n",
    "plt.imshow(det_ideal) # 查看探测器层"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df71fac",
   "metadata": {},
   "source": [
    "### ***D2NN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "edd609c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先做一个模型初始化，将生成的初始随机相位参数与模型分离，以便于后面对其他参数分别训练。\n",
    "# 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "num_layers = 1\n",
    "distance_between_layers = 0.005, 0.005, 0.005, 0.005, 0.005, 0.005\n",
    "phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "                                                          astype('float32'))) for _ in range(num_layers)]\n",
    "# Amp = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "#                                                           astype('float32'))) for _ in range(num_layers)]\n",
    "# 初始化层间距，预训练时（先只训练相位）设置梯度requires_grad=False。\n",
    "distance = [torch.nn.Parameter(distance_between_layers[i]*torch.tensor([1])) for i in range(num_layers+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b18f4097",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0.0050], requires_grad=True)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "6d42025c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DNN(torch.nn.Module):\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    phase & amplitude modulation\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    def __init__(self, phase = [], num_layers = 1, wl = 532e-9, N_pixels = 64, pixel_size = 20e-6, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN, self).__init__()\n",
    "        \n",
    "        # 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "#         self.phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "#                                                           astype('float32')-0.5)) for _ in range(num_layers)]\n",
    "        # 向网络中添加每层相位板的参数\n",
    "        for i in range(num_layers):\n",
    "            self.register_parameter(\"phase\" + \"_\" + str(i), phase[i])\n",
    "        # 向网络中添加每层相位板的衰减参数\n",
    "#         for i in range(num_layers):\n",
    "#             self.register_parameter(\"Amp\" + \"_\" + str(i), Amp[i])\n",
    "        # 向网络中添加层间距的参数\n",
    "        for i in range(num_layers+1):\n",
    "            self.register_parameter(\"distance\" + \"_\" + str(i), distance[i])\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            constr_phase = 2*np.pi*phase[index]\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*np.pi*torch.sigmoid(phase[index])\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase\n",
    "        E = self.last_diffractive_layer(E)\n",
    "        Int = torch.abs(E)**2\n",
    "#         output = self.sofmax(detector_region(Int))\n",
    "        output = detector_region(Int)\n",
    "        return output, Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b7d6ff21",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN(\n",
      "  (diffractive_layers): ModuleList(\n",
      "    (0): Diffractive_Layer()\n",
      "  )\n",
      "  (last_diffractive_layer): Propagation_Layer()\n",
      "  (sofmax): Softmax(dim=-1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = DNN(phase = phase, distance = distance).to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b2a44609",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase_0\n",
      "distance_0\n",
      "distance_1\n"
     ]
    }
   ],
   "source": [
    "# 展示可供训练的参数（因为）\n",
    "for index, item in model.named_parameters():\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "231af38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n"
     ]
    }
   ],
   "source": [
    "# 先只训练相位，层间距先设定0.005。之后再在0.005这个基础上微调训练层间距。\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if name.find('phase') != -1:\n",
    "        exec('model.' + name + '.requires_grad_(True)')\n",
    "# for name, param in model.named_parameters():\n",
    "#     if name.find('Amp') != -1:\n",
    "#         exec('model.' + name + '.requires_grad_(True)')\n",
    "    \n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abed7f3b",
   "metadata": {},
   "source": [
    "### ***Training***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4551157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义训练函数\n",
    "def train(model, loss_function, optimizer, trainloader, testloader, epochs = 10, device = 'cpu', filename = 'best.pt'):\n",
    "    train_loss_hist = []\n",
    "    test_loss_hist = []\n",
    "    train_acc_hist = []\n",
    "    test_acc_hist = []\n",
    "    best_acc = 0\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    for epoch in range(epochs):\n",
    "        ep_loss = 0\n",
    "        # 每个epoch开始时启动Batch_Normalization和Dropout。BN层能够用到每一批数据的均值和方差，Dropout随机取一部分网络连接来训练更新参数。\n",
    "        model.train()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        # 加载进度条\n",
    "        for images, labels in tqdm(trainloader):\n",
    "            \n",
    "            images = images.to(device).squeeze()\n",
    "#             images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "            labels = labels.to(device)\n",
    "            det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "#             det_labels = labels_image_tensors[labels] # 定义各标签的探测器层张量\n",
    "            \n",
    "            optimizer.zero_grad() # 梯度清零\n",
    "            out_label, out_img = model(images) # 得到预测各个探测器上的光强分布以及探测层光强分布\n",
    "            \n",
    "            _, predicted = torch.max(out_label, 1) # 找到光强分布占比最大的探测器，并作为预测结果\n",
    "            correct += (predicted == labels).sum().item() # 得到一个batch的预测结果。如果预测值等于标签值，则正确值加一。\n",
    "            total += labels.size(0) # 得到一个batch的标签总数\n",
    "\n",
    "            full_int_img = out_img.sum(axis=(1,2))\n",
    "#             loss = loss_function(out_img/full_int_img[:,None,None], det_labels) # 光强分布归一化后送入损失函数（与完美探测结果进行比较）\n",
    "#             loss = loss_function(out_label, det_labels)\n",
    "            loss = loss_function(out_label, det_labels*0.1)\n",
    "            \n",
    "            loss.backward() # 反向传播\n",
    "            optimizer.step() # 参数更新\n",
    "            ep_loss += loss.item() # 更新本次epoch的损失\n",
    "        train_loss_hist.append(ep_loss /len(trainloader)) # 计算平均损失\n",
    "        train_acc_hist.append(correct /total) # 计算准确率\n",
    "        #train_acc_hist.append(validate(model, trainloader,device))\n",
    "\n",
    "        #test_acc_hist.append(validate(model, testloader,device))\n",
    "        # if test_acc_hist[-1][0]>best_acc:\n",
    "        #     best_model=copy.deepcopy(model)\n",
    "        \n",
    "        ep_loss = 0\n",
    "        # 不启用Batch Normalization和Dropout。测试过程中要保证BN层的均值和方差不变，且利用到了所有网络连接，即不进行随机舍弃神经元。\n",
    "        model.eval()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        with torch.no_grad(): # 停止梯度更新\n",
    "            for images, labels in tqdm(testloader):\n",
    "                images = images.to(device).squeeze()\n",
    "#                 images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "                labels = labels.to(device)\n",
    "                det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "#                 det_labels = labels_image_tensors[labels]\n",
    "\n",
    "                out_label, out_img = model(images)\n",
    "                _, predicted = torch.max(out_label, 1)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "                total += labels.size(0)\n",
    "\n",
    "                full_int_img = out_img.sum(axis=(1,2))\n",
    "#                 loss = loss_function(out_img/full_int_img[:,None,None], det_labels)\n",
    "#                 loss = loss_function(out_label, det_labels)\n",
    "                loss = loss_function(out_label, det_labels*0.01)\n",
    "                \n",
    "                #直接计算损失，无反向传播和梯度更新。\n",
    "                ep_loss += loss.item()\n",
    "        test_loss_hist.append(ep_loss / len(testloader))\n",
    "        test_acc_hist.append(correct / total)\n",
    "        # 如果最后一次训练的准确率大于之前最好的准确率，则将最后一次的模型保存为最佳模型。\n",
    "        if test_acc_hist[-1]>best_acc:\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            state = {\n",
    "              'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重\n",
    "              'best_acc': best_acc,\n",
    "              'optimizer' : optimizer.state_dict(),\n",
    "            }\n",
    "            torch.save(state, filename)\n",
    "\n",
    "        print(f\"Epoch={epoch} train loss={train_loss_hist[epoch]:.4}, test loss={test_loss_hist[epoch]:.4}\")\n",
    "        print(f\"train acc={train_acc_hist[epoch]:.4}, test acc={test_acc_hist[epoch]:.4}\")\n",
    "        print(\"-----------------------\")\n",
    "        \n",
    "    # 训练完后用最好的一次当做模型最终的结果,等着一会测试\n",
    "    model.load_state_dict(best_model_wts)    \n",
    "    return train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5f71b821",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_loss(imgs, det_labels):\n",
    "    full_int = imgs.sum(dim=(1,2))\n",
    "    loss = 1 - (imgs*det_labels).sum(dim=(1,2))/full_int\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "47e0c342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义衍射模型基本参数\n",
    "wl = 532e-9\n",
    "pixel_size = 10*wl\n",
    "# 定义模型，损失函数和优化器\n",
    "model = DNN(phase = phase, num_layers = 1, wl = wl, pixel_size = pixel_size, distance = distance).to(device)\n",
    "# criterion = custom_loss\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=0.002)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9ce007f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:13<00:00, 21.96it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=17.58, test loss=20.71\n",
      "train acc=0.6974, test acc=0.8791\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.17it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=16.85, test loss=20.71\n",
      "train acc=0.8965, test acc=0.9163\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:13<00:00, 21.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=16.75, test loss=20.72\n",
      "train acc=0.9189, test acc=0.9282\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=3 train loss=16.71, test loss=20.75\n",
      "train acc=0.9268, test acc=0.934\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:13<00:00, 21.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=4 train loss=16.68, test loss=20.76\n",
      "train acc=0.9319, test acc=0.938\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:13<00:00, 21.55it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=5 train loss=16.66, test loss=20.75\n",
      "train acc=0.9359, test acc=0.9399\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.32it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=6 train loss=16.65, test loss=20.78\n",
      "train acc=0.9381, test acc=0.9414\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.42it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=7 train loss=16.64, test loss=20.78\n",
      "train acc=0.9396, test acc=0.9418\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:13<00:00, 21.44it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 25.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=8 train loss=16.63, test loss=20.79\n",
      "train acc=0.9407, test acc=0.9413\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=9 train loss=16.62, test loss=20.79\n",
      "train acc=0.9419, test acc=0.9438\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.10it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=10 train loss=16.62, test loss=20.8\n",
      "train acc=0.9426, test acc=0.9438\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 20.89it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=11 train loss=16.61, test loss=20.8\n",
      "train acc=0.9428, test acc=0.9453\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=12 train loss=16.61, test loss=20.79\n",
      "train acc=0.9432, test acc=0.9459\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.33it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=13 train loss=16.61, test loss=20.8\n",
      "train acc=0.9447, test acc=0.9471\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=14 train loss=16.6, test loss=20.8\n",
      "train acc=0.945, test acc=0.9485\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.24it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=15 train loss=16.6, test loss=20.81\n",
      "train acc=0.9456, test acc=0.9466\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.10it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 25.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=16 train loss=16.6, test loss=20.81\n",
      "train acc=0.9455, test acc=0.949\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.11it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=17 train loss=16.6, test loss=20.8\n",
      "train acc=0.9461, test acc=0.9479\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 20.94it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=18 train loss=16.59, test loss=20.82\n",
      "train acc=0.9464, test acc=0.9462\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.31it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 26.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=19 train loss=16.59, test loss=20.81\n",
      "train acc=0.9465, test acc=0.9485\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 正式开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 20,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1b62795d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('phase_0', Parameter containing:\n",
      "tensor([[ 0.1956,  0.0693,  0.7374,  ..., -0.3684,  0.6065,  0.7606],\n",
      "        [ 0.4205,  0.4169, -0.0839,  ...,  1.0482,  0.7337, -0.2853],\n",
      "        [ 0.4496,  0.6010, -0.0175,  ..., -0.0564,  0.4955, -0.1442],\n",
      "        ...,\n",
      "        [ 0.0700,  0.6225,  0.5487,  ...,  0.0439, -0.1260, -0.2706],\n",
      "        [ 0.3333,  0.1393,  0.3942,  ...,  0.6930,  0.6111,  0.6799],\n",
      "        [-0.3273,  0.6731,  0.7949,  ...,  0.6327,  0.0060,  0.7368]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('distance_0', Parameter containing:\n",
      "tensor([0.0050], device='cuda:0'))\n",
      "('distance_1', Parameter containing:\n",
      "tensor([0.0050], device='cuda:0'))\n"
     ]
    }
   ],
   "source": [
    "# 查看训练的各个参数，确保这一次只有相位被训练了（requires_grad=True）\n",
    "for param in model.named_parameters():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5a17d46d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n",
      "\t distance_0\n",
      "\t distance_1\n"
     ]
    }
   ],
   "source": [
    "# 全部解禁，相位和层间距都拿来训练\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = True\n",
    "\n",
    "# for name, param in model.named_parameters():\n",
    "#     if name.find('distance') != -1:\n",
    "#         exec('model.' + name + '.requires_grad_(True)')\n",
    "\n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ca35e2ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载之前训练好的参数\n",
    "checkpoint = torch.load('best.pt')\n",
    "best_acc = checkpoint['best_acc']\n",
    "model.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "717642ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载损失函数和优化器\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "98f7eb5b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 21.14it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 25.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=16.59, test loss=20.81\n",
      "train acc=0.9478, test acc=0.949\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 20.61it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 26.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=16.59, test loss=20.81\n",
      "train acc=0.9478, test acc=0.9488\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 20.91it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 26.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=16.59, test loss=20.82\n",
      "train acc=0.9478, test acc=0.9495\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:14<00:00, 20.82it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:01<00:00, 26.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=3 train loss=16.59, test loss=20.81\n",
      "train acc=0.9478, test acc=0.9488\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 重新开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 4,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "344e84fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[ 0.1956,  0.0693,  0.7374,  ..., -0.3684,  0.6065,  0.7606],\n",
      "        [ 0.4205,  0.4169, -0.0839,  ...,  1.0482,  0.7337, -0.2853],\n",
      "        [ 0.4496,  0.6010, -0.0175,  ..., -0.0564,  0.4955, -0.1442],\n",
      "        ...,\n",
      "        [ 0.0700,  0.6225,  0.5487,  ...,  0.0439, -0.1260, -0.2706],\n",
      "        [ 0.3333,  0.1393,  0.3942,  ...,  0.6930,  0.6111,  0.6799],\n",
      "        [-0.3273,  0.6731,  0.7949,  ...,  0.6327,  0.0060,  0.7368]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0050], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0050], device='cuda:0', requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa9faa1",
   "metadata": {},
   "source": [
    "### ***Saving***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0ec77081",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'MNIST_0.95-single-layer.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "239983a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 释放显存\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619ac490",
   "metadata": {},
   "source": [
    "### ***Loading***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "771976ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('MNIST_0.95-single-layer.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d61eb80",
   "metadata": {},
   "source": [
    "### ***Data Analysis***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1e175e1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00500881\n",
      "0.00500075\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if name.find('distance') != -1:\n",
    "        print('{:.8f}'.format(param.cpu().detach().numpy()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "adac227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t phase_0\n",
      "\t distance_0\n",
      "\t distance_1\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ff703d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 4.5)\n",
    "def visualize(image, label):\n",
    "    image_E = torch.sqrt(image)\n",
    "    out = model(image_E.to(device))\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)\n",
    "    ax1.imshow(image.squeeze(), interpolation='none')\n",
    "    ax1.set_title(f'Input image with\\n total intensity {image.squeeze().sum():.2f}')\n",
    "    output_image = torch.abs(out[1].squeeze()).detach().cpu()\n",
    "    ax2.imshow(output_image*det_ideal, interpolation='none')\n",
    "    ax2.set_title(f'Output image with\\n total intensity {output_image.squeeze().sum():.2f}')\n",
    "    dist = out[0].squeeze().detach().cpu()\n",
    "    ax3.bar(range(len(dist)), dist)\n",
    "    fig.suptitle(\"label={}\".format(label))\n",
    "    plt.show()\n",
    "\n",
    "def mask_visualiztion():\n",
    "    for name, mask in model.named_parameters():\n",
    "        if name.find('phase') != -1:\n",
    "            plt.imshow(torch.sigmoid(mask.detach().cpu())*360, interpolation = 'none')\n",
    "            plt.title(f'Mask of layer {name}')\n",
    "            plt.colorbar()\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "38979bf8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_visualiztion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7343f851",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_ind = np.random.choice(range(len(val_dataset)), size=10, replace=False)\n",
    "for ind in rand_ind:\n",
    "    visualize(val_dataset[ind][0], val_dataset[ind][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "fb04216d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(predicted, labels, conf_matrix):\n",
    "    for p, t in zip(predicted, labels):\n",
    "        conf_matrix[p, t] += 1\n",
    "    return conf_matrix\n",
    "#首先定义一个 分类数*分类数 的空混淆矩阵\n",
    "conf_matrix = torch.zeros(classes_num, classes_num)\n",
    "# 使用torch.no_grad()可以显著降低测试用例的GPU占用\n",
    "with torch.no_grad(): # 停止梯度更新\n",
    "    for i, (images, labels) in enumerate(val_dataloader):\n",
    "        images = images.to(device)\n",
    "        images = torch.sqrt(torch.squeeze(images))\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        out_labels, out_images = model(images)\n",
    "        _, predicted = torch.max(out_labels.data, 1)\n",
    "        \n",
    "        #记录混淆矩阵参数\n",
    "        conf_matrix = confusion_matrix(predicted, labels, conf_matrix)\n",
    "        conf_matrix = conf_matrix.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d4cd8a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵总元素个数：10000,测试集总个数:10000\n",
      "[[ 966.    0.    9.    2.    0.   15.    6.    4.   10.   11.]\n",
      " [   0. 1109.    1.    0.    4.    0.    2.   10.    1.    9.]\n",
      " [   0.    5.  973.   13.    4.    3.    1.   20.    3.    0.]\n",
      " [   0.    2.   12.  938.    1.   34.    0.    3.    9.   10.]\n",
      " [   0.    1.    4.    2.  909.    2.    6.    4.    6.   14.]\n",
      " [   2.    0.    0.   11.    0.  790.   10.    2.    5.    8.]\n",
      " [   5.    6.    5.    3.   26.   15.  929.    3.    8.    2.]\n",
      " [   1.    0.    9.    8.    0.    7.    0.  952.    4.    5.]\n",
      " [   5.   11.   19.   29.    8.   23.    4.    8.  926.   24.]\n",
      " [   1.    1.    0.    4.   30.    3.    0.   22.    2.  926.]]\n",
      "每种标签总个数：1023  1136  1022  1009  948  828  1002  986  1057  989\n",
      "每种标签预测正确的个数：966  1109  973  938  909  790  929  952  926  926\n",
      "每种标签的识别准确率为：94.4%  97.6%  95.2%  93.0%  95.9%  95.4%  92.7%  96.6%  87.6%  93.6%"
     ]
    }
   ],
   "source": [
    "conf_matrix = np.array(conf_matrix)# 将混淆矩阵从gpu转到cpu再转到np\n",
    "corrects = conf_matrix.diagonal(offset = 0)#抽取对角线的每种分类的识别正确个数\n",
    "per_classes = conf_matrix.sum(axis = 1)#抽取每个分类数据总的测试条数\n",
    "\n",
    "print(\"混淆矩阵总元素个数：{},测试集总个数:{}\".format(int(np.sum(conf_matrix)), BATCH_SIZE*len(val_dataloader)))\n",
    "np.set_printoptions(suppress=True) \n",
    "print(conf_matrix)\n",
    "\n",
    "# 获取每种label的识别准确率\n",
    "percent = [rate*100 for rate in corrects/per_classes]\n",
    "per_classes = list(per_classes)\n",
    "corrects = list(corrects)\n",
    "\n",
    "print(\"每种标签总个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in per_classes])))\n",
    "print(\"每种标签预测正确的个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in corrects])))\n",
    "print(\"每种标签的识别准确率为：{}\".format('%  '.join(['{:.1f}'.format(i) for i in percent])), end='%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "46a9d760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "label_ticks = list()\n",
    "for i in range(classes_num):\n",
    "    label_ticks = label_ticks + ['{}'.format(classes[i])]\n",
    "\n",
    "# 显示数据\n",
    "plt.imshow(conf_matrix, cmap=plt.cm.Blues)\n",
    "thresh = conf_matrix.max() / 2\t#数值颜色阈值，如果数值超过这个，就颜色加深。\n",
    "for i in range(classes_num):\n",
    "    for j in range(classes_num):\n",
    "        info = int(conf_matrix[j, i])\n",
    "        plt.text(i, j, info,\n",
    "                 verticalalignment='center',\n",
    "                 horizontalalignment='center',\n",
    "                 color=\"white\" if info > thresh else \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "85bd5d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将模型中的phase导出来\n",
    "phase = []\n",
    "for param in model.named_parameters():\n",
    "    phase.append(param[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "0c82e980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 64])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "2e4d0f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 写一个测试模型，用于查看中间层的输出图案\n",
    "class DNN_test(torch.nn.Module):\n",
    "    def __init__(self, phase=[], num_layers=5, wl = 532e-9, N_pixels = 64, pixel_size = 10*wl, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN_test, self).__init__()\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        E_out = []\n",
    "        Int = []\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*torch.pi*torch.sigmoid(self.phase[index])\n",
    "            constr_phase = 2*torch.pi*phase[index]\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase\n",
    "            E_out.append(E)\n",
    "        E_out.append(self.last_diffractive_layer(E_out[-1]))\n",
    "        for i in range(len(E_out)):\n",
    "            Int.append(torch.abs(E_out[i])**2)\n",
    "        return Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "45221648",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_test = DNN_test(phase = phase, num_layers = 1, wl = wl, pixel_size = pixel_size, distance = distance).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a08c96aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mid_img = model_test(images_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "66f84499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "cead7a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mid_img[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "9b954ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22e6a5930a0>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品的原图\n",
    "plt.imshow(images_E[0].cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7b6e861a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22e5548a370>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品在第1层的图案\n",
    "plt.imshow(mid_img[1][0].cpu().detach().numpy()*det_ideal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b551ae2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "D2NN",
   "language": "python",
   "name": "pytorch"
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    "name": "ipython",
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